Microplastics, known as plastic fragments of size less than 5 mm, are found to be troubling contaminants in the water bodies and pose pollution risks and threats to the environment and human health. This review evaluates and discusses the distribution, typical sources, identification, and remediation of microplastics in the drinking water and fresh grounds waters. Microplastic contamination on the water is usually associated with urban runoff or stormwater, waste water, plastic recycling or breakdown of large plastic parts. The benefits of using some of the techniques including spectroscopy and microscopy in detecting microplastics are outlined while some of the shortcomings of these methods are highlighted too. Different techniques for removing microplastic contamination, including cooperative and filter membrane methods, and advanced oxidation methods, are reviewed. Among these, coagulation and filtration are the most common due to their low cost and ease of use. The amount of microplastics found in the environment particularly the breakdown of the microplastics remains the first challenge despite several advanced detection and treatment technologies. This review called for the need to design and adopt standardized systems for addressing microplastic pollution and microplastic removal from waters due to its implications on health and water.

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This document is about Brain tumor identification plays a crucial role in medical image processing, where leveraging deep learning methodologies, particularly Convolutional Neural Networks (CNNs), has demonstrated remarkable potential. Detecting tumors at an early stage with high accuracy is essential for optimizing treatment strategies and enhancing patient prognosis. CNNs facilitate automated, precise, and efficient analysis by extracting critical features from medical scans, enabling accurate tumor categorization. This abstract outline deep learning approaches based on CNNs for detecting brain tumors, emphasizing their structural designs, effectiveness, and impact on medical diagnosis.

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Firefighting robots have emerged as a critical innovation to enhance safety, efficiency, and effectiveness in fire suppression operations. This paper presents a comprehensive analysis of the current advancements in autonomous firefighting robots, focusing on their fire detection capabilities, navigation systems, extinguishing mechanisms, and collaborative operation in large-scale scenarios. Drawing upon recent studies, the integration of machine learning, SLAM-based navigation, and multi-robot coordination is explored to present a cohesive framework for the design and implementation of fire extinguisher robotic systems.

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Phishing emails represent one of the largest hazards in today's world resulting in billions of unwanted monetary losses. While phishing emails detection methods are constantly being assessed, the current results for those methods are not very good. Additionally, phishing emails records show that phishing emails are escalating at an unmanageable speed each year. Accordingly, we need better phishing detection systems to help with the phishing email threat. In this study, we first researched the modality of an email. Then based on an improved Recurrent Convolutional Neural Networks (RCNN), with multilevel vectors and attention mechanism, we proposed a new phishing email detection model called, by looking at the emails content in parallel, including from the email header, the email body, the character level, and the word level. We also used the unbalanced dataset realistic phishing to legitimate email problem as a basis on if the classifier was effective. The results show we proved the effectiveness of, as it provides a way to filter phishing emails with high degree of confidence to pick out the phishing emails, and filter legitimate emails as little as possible. Overall, that is a promising result to perform better than existing methods and provide assurance of effectiveness of in detecting phishing emails.

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The increasing need for accurate and efficient multi-person video tracking has led to much research in the optimization of tracking models for surveillance, healthcare, sports analytics, and behavioral analysis. However, despite the impressive progress made so far, the existing reviews within the domain rarely provide a structured taxonomy that can compare different models across key performance metrics such as accuracy, occlusion handling, real-time efficiency, and adaptability. Additionally, previous reviews lack a systematic analysis of deep learning-driven approaches, hybrid methodologies, and their integration with decentralized frameworks such as block-chain. This work fills these gaps by conducting a comprehensive, iterative taxonomy-based review of recent state-of-the-art multi-person tracking models, evaluating them under a PRISMA-driven framework process. This structured review provides an in-depth comparative analysis, identifying optimal models for real-time tracking, medical diagnostics, and blockchain-based decentralization. It has contributed to the development of next-generation tracking solutions by guiding researchers toward integrating hybrid AI models, decentralized computing, and energy-efficient tracking mechanisms that enhance tracking accuracy, security, and scalability in real-world scenarios.

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Urbanization in the summary leads to an increase in the generation of waste, which represents an important challenge for sustainable development. In this article, we will consider the design and implementation of intelligent waste management systems (IWMs) that use technologies such as the Internet of Things (IoT), artificial intelligence (AI), and cloud computing. IWMS hopes to optimize waste collection, improve recycling efforts and reduce environmental impact by integrating intelligent sensors, real data analysis and automation processes. This study presents a system architecture, describes implementation strategies, and evaluates the potential benefits and challenges associated with the use of IWM in urban environments.

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In this article is show the right path of the preparation of a series of 2, 3-di-substituted quinazolin-4-(3H)-ones analogous has been employed in a one pot by reacting 2-amino-N-phenyl benzamide with different substituted aromatic aldehydes in ethanol as a solvent promoted by CAS a Lewis acid catalyst and this procedure is straight forward and an effective synthesis. In addition to the analogous was evaluated by antibacterial activity and also the final analogous was analyzed by advanced spectroscopic data ¹HNMR, ¹³CNMR, and mass. The advantaged of this approach was offers a greater number of benefits, including a high yield, a quick reaction time, mild reaction conditions, ease of operation, an easy work-up process that is ecofriendly benign, and the development of non-chromatographic methods for product purification.

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Probiotics have many beneficial effects, from preventing diseases to improving general well-being, which adds them as powerful performance enhancers and increases oxygen saturation. This study investigates the relationship between the effects of probiotics and oxygen saturation as well as athletic performance among female athletes between the ages of 17 and 23 years at Vanita Vishram Woman's University. A total of 110 actively participating sportswomen were included in a cross-sectional experimental study. With the use of a structured questionnaire, the information on demographic details, dietary patterns, and anthropometric measurements, oxygen saturation readings were taken both before and after athletic events. To relate the assumption of probiotic intake of performance variables, the Mann-Whitney and Wilcoxon tests were used. Results indicate that probiotic drink may influence oxygen saturation positively and perhaps therefore, whether it affects endurance and recovery more generally or overall performance. The physiological responses of the participants with continuous intake of probiotics were revealed to have improved; supporting the hypothesis that gut microbiota is vital to efficient sports performance. The findings highlight the need for further research into probiotic supplementation as a natural and non-invasive strategy to enhance athletic outcomes. It is recommended that future studies employ larger sample sizes with longer intervention periods, while controlling dietary intake to establish a stronger scientific basis for probiotics as athletic performance enhancers. This research adds to the growing evidence supporting the role of probiotics in sports nutrition and exercise physiology.

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Across many sectors, corrosion is a constant concern that requires efficient mitigation techniques. The application of artificial intelligence (AI) and machine learning (ML) is improving the performance of thermal spray coatings, which have become a flexible option. This study investigates how thermal spray coating might lessen corrosion on high-performance equipment like boilers. Next, the use of AI and ML to forecast coating performance, optimize thermal spray procedures, and enable sophisticated corrosion monitoring. AI/ML algorithms can forecast corrosion behavior, find crucial factors, and help create new, high-performance coatings by evaluating massive datasets. This opens the door to increased corrosion resistance and longer material lifespan.

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